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Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: New. In.

Paperback or Softback. Zustand: New. Numerical Computing with Python 2.54. Book.

Zustand: New. In.

Zustand: New.

Zustand: New.

Paperback or Softback. Zustand: New. Statistics for Machine Learning 1.66. Book.

PF. Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Digital. Zustand: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Digital. Zustand: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Digital. Zustand: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Digital. Zustand: New. Build Machine Learning models with a sound statistical understanding.About This Book. Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Who This Book Is ForThis book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful.What You Will Learn. Understand the Statistical and Machine Learning fundamentals necessary to build models. Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems. Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages. Analyze the results and tune the model appropriately to your own predictive goals. Understand the concepts of required statistics for Machine Learning. Introduce yourself to necessary fundamentals required for building supervised and unsupervised deep learning models. Learn reinforcement learning and its application in the field of artificial intelligence domainIn DetailComplex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more.By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.Style and approachThis practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: New. New. book.

Zustand: New.

Zustand: acceptable. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Acceptable condition! Any other included accessories are also in Acceptable condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear such as cover tears discoloration, staining, marks, scuffs, etc. All pages intact.

Zustand: New.

Paperback. Zustand: New. New. book.

Zustand: good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.

Zustand: New. SUPER FAST SHIPPING.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.